The Significance of the Enteric Microbiome on the Development of Childhood Disease: A Review of Prebiotic and Probiotic Therapies in Disorders of Childhood
Bibliographic record
Abstract
Recent studies have highlighted the fact that the enteric microbiome, the trillions of microbes that inhabit the human digestive tract, has a significant effect on health and disease. Methods for manipulating the enteric microbiome, particularly through probiotics and microbial ecosystem transplantation, have undergone some study in clinical trials. We review some of the evidence for microbiome alteration in relation to childhood disease and discuss the clinical trials that have examined the manipulation of the microbiome in an effort to prevent or treat childhood disease with a primary focus on probiotics, prebiotics, and/or synbiotics (ie, probiotics + prebiotics). Studies show that alterations in the microbiome may be a consequence of events occurring during infancy and/or childhood such as prematurity, C-sections, and nosocomial infections. In addition, certain childhood diseases have been associated with microbiome alterations, namely necrotizing enterocolitis, infantile colic, asthma, atopic disease, gastrointestinal disease, diabetes, malnutrition, mood/anxiety disorders, and autism spectrum disorders. Treatment studies suggest that probiotics are potentially protective against the development of some of these diseases. Timing and duration of treatment, the optimal probiotic strain(s), and factors that may alter the composition and function of the microbiome are still in need of further research. Other treatments such as prebiotics, fecal microbial transplantation, and antibiotics have limited evidence. Future translational work, in vitro models, long-term and follow-up studies, and guidelines for the composition and viability of probiotic and microbial therapies need to be developed. Overall, there is promising evidence that manipulating the microbiome with probiotics early in life can help prevent or reduce the severity of some childhood diseases, but further research is needed to elucidate biological mechanisms and determine optimal treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".